def test_griewank_works_fine(self):
mke_griewank = MonkeyKingEvolutionV3(n=10,
C_a=5,
C_r=0.5,
seed=self.seed)
mke_griewankc = MonkeyKingEvolutionV3(n=10,
C_a=5,
C_r=0.5,
seed=self.seed)
AlgorithmTestCase.algorithm_run_test(self, mke_griewank, mke_griewankc)
def test_custom_works_fine(self):
mke_custom = MonkeyKingEvolutionV3(n=10,
C_a=2,
C_r=0.5,
seed=self.seed)
mke_customc = MonkeyKingEvolutionV3(n=10,
C_a=2,
C_r=0.5,
seed=self.seed)
AlgorithmTestCase.algorithm_run_test(self, mke_custom, mke_customc,
MyBenchmark())
def setUp(self):
self.D = 40
self.mkev3_custom = MonkeyKingEvolutionV3(D=self.D,
nFES=1000,
n=10,
C_a=2,
C_r=0.5,
benchmark=MyBenchmark())
self.mkev3_griewank = MonkeyKingEvolutionV3(D=self.D,
nFES=1000,
n=10,
C_a=5,
C_r=0.5,
benchmark=Griewank())
def test_griewank_works_fine(self):
mke_griewank = MonkeyKingEvolutionV3(D=self.D,
nFES=self.nFES,
nGEN=self.nGEN,
n=10,
C_a=5,
C_r=0.5,
benchmark=Griewank(),
seed=self.seed)
mke_griewankc = MonkeyKingEvolutionV3(D=self.D,
nFES=self.nFES,
nGEN=self.nGEN,
n=10,
C_a=5,
C_r=0.5,
benchmark=Griewank(),
seed=self.seed)
AlgorithmTestCase.algorithm_run_test(self, mke_griewank, mke_griewankc)
def test_custom_works_fine(self):
mke_custom = MonkeyKingEvolutionV3(D=self.D,
nFES=self.nFES,
nGEN=self.nGEN,
n=10,
C_a=2,
C_r=0.5,
benchmark=MyBenchmark(),
seed=self.seed)
mke_customc = MonkeyKingEvolutionV3(D=self.D,
nFES=self.nFES,
nGEN=self.nGEN,
n=10,
C_a=2,
C_r=0.5,
benchmark=MyBenchmark(),
seed=self.seed)
AlgorithmTestCase.algorithm_run_test(self, mke_custom, mke_customc)
# encoding=utf8
# This is temporary fix to import module from parent folder
# It will be removed when package is published on PyPI
import sys
sys.path.append('../')
# End of fix
import random
from NiaPy.algorithms.basic import MonkeyKingEvolutionV3
from NiaPy.util import StoppingTask, OptimizationType
from NiaPy.benchmarks import Sphere
# we will run Nelder Mead algorithm for 5 independent runs
for i in range(5):
task = StoppingTask(D=10,
nGEN=50,
optType=OptimizationType.MINIMIZATION,
benchmark=Sphere())
algo = MonkeyKingEvolutionV3()
best = algo.run(task=task)
print('%s -> %s' % (best[0], best[1]))
# vim: tabstop=3 noexpandtab shiftwidth=3 softtabstop=3
def plot_example(D=10, nFES=50000, nGEN=100000, seed=None, optType=OptimizationType.MINIMIZATION, optFunc=MinMB, **kn):
task = TaskConvPlot(D=D, nFES=nFES, nGEN=nGEN, optType=optType, benchmark=optFunc())
algo = MonkeyKingEvolutionV3(NP=25, C=3, F=0.5, FC=0.5, R=0.4, task=task)
best = algo.run()
logger.info('%s %s' % (best[0], best[1]))
input('Press [enter] to continue')
def simple_example(runs=10, D=10, nFES=50000, nGEN=100000, seed=None, optType=OptimizationType.MINIMIZATION, optFunc=MinMB, **kn):
for i in range(10):
Algorithm = MonkeyKingEvolutionV3(D=D, nFES=nFES, NP=25, C=3, F=0.5, FC=0.5, R=0.4, optType=optType, benchmark=optFunc())
Best = Algorithm.run()
logger.info('%s %s' % (Best[0], Best[1]))